MediumSQL challenge #189SUBQUERY

Price Above Category Avg

Find products priced above their category average.

The challenge runs on the SQLabHub practice database (SQLite) and uses the products table. You can solve it in the browser, with nothing to install.

Solve This SQL Challenge

The task

Find products priced above their category average.

A correct answer returns 63 rows with 10 columns: id, name, category, price, cost, stock, sku, status, rating, review_count.

Tables used in this challenge

`products` 120 rows

id INTEGER (PK) · name TEXT · category TEXT · price REAL · cost REAL · stock INTEGER · sku TEXT · status TEXT · rating REAL · review_count INTEGER

First 3 rows of the products table
id	name	category	price	cost	stock	sku	status	rating	review_count
1	Play Kitchen Deluxe	Toys	1786.38	589.51	257	SKU-000001	active	4	57
2	Micro-Cut Paper Shredder 8-Sheet	Office Supplies	907.93	281.46	131	SKU-000002	active	2.5	447
3	4K Dual Dash Camera	Automotive	568.89	204.8	176	SKU-000003	active	4.1	29

The first rows of each table are shown. See how all the tables relate on the database schema page.

Expected output

Expected output of the challenge
id	name	category	price	cost	stock	sku	status	rating	review_count
1	Play Kitchen Deluxe	Toys	1786.38	589.51	257	SKU-000001	active	4	57
4	Ergonomic Desk Mat XL	Office Supplies	1618.64	663.64	164	SKU-000004	active	4	262
5	Premium Beard Oil 60ml	Health & Beauty	1428.31	428.49	183	SKU-000005	active	5	305
7	Giant Stuffed Teddy Bear	Toys	1130.58	361.79	453	SKU-000007	active	3.1	419
8	Artisan Hot Sauce Trio	Food & Beverage	1698.99	509.7	391	SKU-000008	discontinued	3.9	239

First 5 of 63 rows. This is the output of the model solution on the practice database. The task does not fix a sort order, so your rows may come back in a different order.

What you practise

Returning every column of a table with SELECT *
Filtering rows with a WHERE condition
Summarising many rows into one value with COUNT, SUM, AVG, MIN or MAX
Writing a subquery that refers to the current row of the outer query

Hint and approach

Try the challenge first. Open these only when you need them.

Show the hint

Correlated subquery

Show the step-by-step approach

How the model solution is built, in the order the database evaluates it:

Read from the products table.
Filter in WHERE: keep the rows where price is greater than the value returned by a subquery.
The subquery is correlated: it refers to the current outer row, so it is evaluated for each row. It works like this:
1. Read from the products table.
2. Filter in WHERE: keep the rows where category equals the outer row's category.
3. Aggregate over all the rows that are left: compute AVG(price). With no GROUP BY, the result is a single row.
Return every column with SELECT *.

Model solution

Checking your session…

What to watch for

Correlated subquery. A correlated subquery is evaluated per outer row, which gets slow on large tables. A join to a grouped subquery or a window function usually does the same work in one pass.
Aggregate functions. Aggregates skip NULLs. AVG divides by the number of non-NULL values and COUNT(column) can be smaller than COUNT(*).
WHERE. WHERE is evaluated before the SELECT list, so it cannot use a column alias defined there or the result of an aggregate.